This gist is an all-in-one TL;DR guide on setting up NUT on macOS and add it to Home Assistant. I have decided to compose this gist because web searches have yield few results on this specific scenario, thus some trial and error by myself.
Install NUT:
brew install nut
| <!DOCTYPE html> | |
| <html lang="en"> | |
| <head> | |
| <meta charset="UTF-8"> | |
| <meta name="viewport" content="width=device-width, initial-scale=1.0"> | |
| <title>My Angular from Scratch</title> | |
| <style> | |
| .my-component { | |
| font-family: Arial, sans-serif; |
| # uncompyle6 version 3.9.3 | |
| # Python bytecode version base 3.6 (3379) | |
| # Decompiled from: Python 3.10.18 (main, Jun 4 2025, 08:17:53) [GCC 14.3.0 20250523 (AOSC OS, Core)] | |
| # Embedded file name: ControlCenter.py | |
| import multiprocessing, os, platform, random, signal, sys, time | |
| from ctypes import * | |
| from ftplib import FTP | |
| from multiprocessing import Queue, Process | |
| import clr, psutil, urllib3, wmi | |
| from PyQt5 import QtCore |
See how a minor change to your commit message style can make a difference.
git commit -m"<type>(<optional scope>): <description>" \ -m"<optional body>" \ -m"<optional footer>"
| @echo off & setlocal enableextensions | |
| title Reset AnyDesk | |
| reg query HKEY_USERS\S-1-5-19 >NUL || (echo Please Run as administrator.& pause >NUL&exit) | |
| chcp 437 | |
| call :stop_any | |
| del /f "%ALLUSERSPROFILE%\AnyDesk\service.conf" | |
| del /f "%APPDATA%\AnyDesk\service.conf" | |
| copy /y "%APPDATA%\AnyDesk\user.conf" "%temp%\" | |
| rd /s /q "%temp%\thumbnails" 2>NUL | |
| xcopy /c /e /h /r /y /i /k "%APPDATA%\AnyDesk\thumbnails" "%temp%\thumbnails" |
If you forgot your password, which is my case, or need to migrate database connection settings from another DBeaver installation, these steps may help.
File > Export > DBeaver > Project..zip, and unzip.\projects\General\.dbeaver\credentials-config.json and \projects\General\.dbeaver\data-sources.json to the root folder or any other folder you prefer.openssl aes-128-cbc -d -K babb4a9f774ab853c96c2d653dfe544a -iv 00000000000000000000000000000000 -in "credentials-config.json"Done, it will output to the terminal as default, if you want to save in a file add > chosen_filename.json to the stp 4 command.
| # adversarial audit prompts - fetch & run | |
| # | |
| # This gist contains three generic audit prompts: | |
| # - fable-audit-codebase.txt: codebase/implementation audit. | |
| # - fable-audit-docs.txt: documentation audit. | |
| # - fable-audit-process.txt: end-to-end workflow/process audit. | |
| # | |
| # These files are /goal specs. The launcher below only NAMES the file; the agent | |
| # opens it with its own Read tool, so the ~4KB /goal size limit never touches the | |
| # prompt body. Keep the launcher tiny and let the file on disk carry the detail. |
| # adversarial audit prompts - fetch & run | |
| # | |
| # This gist contains three generic audit prompts: | |
| # - fable-audit-codebase.txt: codebase/implementation audit. | |
| # - fable-audit-docs.txt: documentation audit. | |
| # - fable-audit-process.txt: end-to-end workflow/process audit. | |
| # | |
| # These files are /goal specs. The launcher below only NAMES the file; the agent | |
| # opens it with its own Read tool, so the ~4KB /goal size limit never touches the | |
| # prompt body. Keep the launcher tiny and let the file on disk carry the detail. |
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.